Assessment of antimicrobial prescribing practice, knowledge, and culture in three teaching hospitals
Bibliographic record
Abstract
BACKGROUND: Antimicrobial resistance (AR) is one of the most critical threats to global health. One of its root causes, misuse of antibiotics, can stem from prescribers' preconceived ideas, differing attitudes, and lack of knowledge. Canadian data on this subject are scarce. This study aimed to understand the culture and knowledge of antimicrobial prescribing to optimize strategies targeting prescribers in the local antimicrobial stewardship program (ASP). METHODS: An anonymous online survey was developed and distributed to antimicrobials prescribers at three acute-care teaching hospitals. The questionnaire surveyed perception of AR and ASPs. RESULTS: A total of 440 respondents completed the entire survey. All agreed that AR is a significant challenge in Canada. The vast majority (86%) of respondents believed that AR is a significant problem at their working hospital. However, only 36% of respondents believed that antibiotics are misused locally. Most (92%) agreed that ASPs can decrease AR. Several knowledge gaps were identified through clinical questions. For example, respondents failed to identify treatment indications for asymptomatic bacteriuria 15% of the time and 59% chose an unnecessarily broad antibiotic when presented a microbiology report with susceptibility results associated with a common clinical syndrome. Prescribers' self-reported confidence did not correlate with their knowledge score. CONCLUSIONS: Respondents recognized AR as a critical issue but awareness and knowledge on antibiotic misuse were lacking. As shown in previous studies, respondents see the threat of AR in a more theoretical way. This study provided a better understanding of antimicrobial prescribing practices and ways to optimize them within three teaching hospitals in Montréal. Barriers to optimal antimicrobial prescribing were identified and strategies for improving the effectiveness of the ASP will be developed accordingly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".